BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
원문 초록
This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67% on the provided test data. It finished at the 2nd place in the competition, without any hand-crafted features, only 0.2% behind the winner.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Rumour Evaluation with Very Large Language Models
SCRum-9: Multilingual Stance Classification over Rumours on Social Media
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives
Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution
WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining
토론
이 논문과 연결된 포럼 글이 아직 없습니다.